Substitution

Deletion

Imputation

Normalization

Effective Data Analysis

Exploratory Data Analysis

Explanatory Data Application

Enhanced Data Assessment

NumPy

Matplotlib

SciPy

Pandas

The 'dropna()' method filters out duplicate values from a DataFrame or Series.

The 'dropna()' method adds missing values to a DataFrame or Series.

The 'dropna()' method removes missing values from a DataFrame or Series.

The 'dropna()' method sorts the values in a DataFrame or Series.

Outlier detection is only relevant for large datasets.

Outlier detection reduces the need for data analysis.

Outlier detection improves data quality and accuracy in analysis.

Outlier detection is primarily used for data visualization.

Tableau

Power BI

Google Data Studio

Excel Charts

To visualize relationships between two variables and identify patterns or correlations.

To summarize data using averages and totals.

To create a timeline of events in the dataset.

To display the distribution of a single variable.

Use histograms, box plots, or density plots with Matplotlib to visualize the distribution of a dataset.

Use line graphs to show trends over time.

Create pie charts to represent categorical data.

Display scatter plots for correlation analysis.

NumPy is a programming language for data visualization.

NumPy is primarily used for web development.

NumPy is a database management system.

NumPy provides efficient array operations and mathematical functions, crucial for data analytics.

load_csv

import_csv

read_csv

fetch_csv

A bar chart is used for displaying percentages, while a histogram is for averages.

A bar chart is for categorical data, and a histogram is for numerical data distribution.

A bar chart uses continuous data, and a histogram uses discrete data.

A bar chart displays trends over time, while a histogram shows categories.

Use line graphs to represent categorical data.

Apply scatter plots for categorical data visualization.

Utilize histograms for displaying categorical information.

Use bar charts, pie charts, or box plots to visualize categorical data.

The purpose of using a box plot is to summarize and compare the distribution of data.

To illustrate the correlation between two variables

To show the exact values of each data point

To display only the mean of the data

filter

groupby

aggregate

sort

Seaborn simplifies the creation of complex visualizations and improves aesthetics compared to Matplotlib.

Seaborn is slower than Matplotlib for rendering plots.

Seaborn does not support statistical visualizations.

Matplotlib has better default color palettes than Seaborn.

Call plt.display() to show the plot

Only import NumPy for data preparation

Import Matplotlib, prepare data, use plt.plot(), and plt.show()

Use plt.scatter() instead of plt.plot()

To increase data redundancy for faster access.

To create backups of the data regularly.

The purpose of data normalization is to minimize data redundancy and ensure data integrity.

To encrypt sensitive data for security purposes.

numpy.median()

numpy.mean()

numpy.sum()

numpy.average()

Correlation only applies to linear relationships.

Correlation is significant in exploratory data analysis as it reveals relationships between variables.

Correlation measures the strength of causation between variables.

Correlation is irrelevant in data analysis and should be ignored.

To visualize data in a graphical format.

To merge two DataFrames into one.

To filter rows based on specific conditions.

To create a new DataFrame by reshaping the existing data.

Line plot

Bar chart

Box plot

3D scatter plot

To group data based on certain criteria.

To replace missing values with a specified value or method.

To remove rows with missing values from a DataFrame.

To sort the DataFrame by a specific column.

Pandas is primarily used for creating visualizations.

Pandas is slower than using raw Python lists for data manipulation.

Pandas allows for easy handling of large datasets with its DataFrame structure.

Pandas does not support time series data.

Box plot

Line plot

Bar chart

Histogram

To provide a summary of statistics for numerical columns in a DataFrame.

To visualize data in a graphical format.

To filter rows based on specific conditions.

To merge two DataFrames into one.

Heatmaps are only useful for categorical data.

Heatmaps do not convey any information about data distribution.

Heatmaps provide a visual representation of data density and relationships.

Heatmaps are primarily used for time series analysis.

To create a new DataFrame by merging multiple DataFrames.

To aggregate data based on one or more keys.

To visualize data in a graphical format.

To sort the DataFrame by a specific column.

merge()

join()

concat()

append()

Scatter plot

Box plot

Line plot

Bar chart